Manjari Narayan

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimers disease. The Alzheimers disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state‐of‐the‐art in predicting cognitive outcomes in Alzheimers disease based on high dimensional, publicly available genetic and structural imaging data. This meta‐analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

Neural Networks of Colored Sequence Synesthesia

Synesthesia is a condition in which normal stimuli can trigger anomalous associations. In this study, we exploit synesthesia to understand how the synesthetic experience can be explained by subtle changes in network properties. Of the many forms of synesthesia, we focus on colored sequence synesthesia, a form in which colors are associated with overlearned sequences, such as numbers and letters (graphemes). Previous studies have characterized synesthesia using resting-state connectivity or stimulus-driven analyses, but it remains unclear how network properties change as synesthetes move from one condition to another. To address this gap, we used functional MRI in humans to identify grapheme-specific brain regions, thereby constructing a functional “synesthetic” network. We then explored functional connectivity of color and grapheme regions during a synesthesia-inducing fMRI paradigm involving rest, auditory grapheme stimulation, and audiovisual grapheme stimulation. Using Markov networks to represent direct relationships between regions, we found that synesthetes had more connections during rest and auditory conditions. We then expanded the network space to include 90 anatomical regions, revealing that synesthetes tightly cluster in visual regions, whereas controls cluster in parietal and frontal regions. Together, these results suggest that synesthetes have increased connectivity between grapheme and color regions, and that synesthetes use visual regions to a greater extent than controls when presented with dynamic grapheme stimulation. These data suggest that synesthesia is better characterized by studying global network dynamics than by individual properties of a single brain region.

Resting state functional MRI reveals abnormal network connectivity in neurofibromatosis 1

Neurofibromatosis type I (NF1) is a genetic disorder caused by mutations in the neurofibromin 1 gene at locus 17q11.2. Individuals with NF1 have an increased incidence of learning disabilities, attention deficits, and autism spectrum disorders. As a single‐gene disorder, NF1 represents a valuable model for understanding gene–brain–behavior relationships. While mouse models have elucidated molecular and cellular mechanisms underlying learning deficits associated with this mutation, little is known about functional brain architecture in human subjects with NF1. To address this question, we used resting state functional connectivity magnetic resonance imaging (rs‐fcMRI) to elucidate the intrinsic network structure of 30 NF1 participants compared with 30 healthy demographically matched controls during an eyes‐open rs‐fcMRI scan. Novel statistical methods were employed to quantify differences in local connectivity (edge strength) and modularity structure, in combination with traditional global graph theory applications. Our findings suggest that individuals with NF1 have reduced anterior–posterior connectivity, weaker bilateral edges, and altered modularity clustering relative to healthy controls. Further, edge strength and modular clustering indices were correlated with IQ and internalizing symptoms. These findings suggest that Ras signaling disruption may lead to abnormal functional brain connectivity; further investigation into the functional consequences of these alterations in both humans and in animal models is warranted. Hum Brain Mapp 36:4566–4581, 2015.

Test-retest reliability of transcranial magnetic stimulation EEG evoked potentials

BACKGROUND Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs), recorded using electroencephalography (TMS-EEG), offer a powerful tool for measuring causal interactions in the human brain. However, the test-retest reliability of TEPs, critical to their use in clinical biomarker and interventional studies, remains poorly understood. OBJECTIVE/HYPOTHESIS We quantified TEP reliability to: (i) determine the minimal TEP amplitude change which significantly exceeds that associated with simply re-testing, (ii) locate the most reliable scalp regions of interest (ROIs) and TEP peaks, and (iii) determine the minimal number of TEP pulses for achieving reliability. METHODS TEPs resulting from stimulation of the left dorsolateral prefrontal cortex were collected on two separate days in sixteen healthy participants. TEP peak amplitudes were compared between alternating trials, split-halves of the same run, two runs five minutes apart and two runs on separate days. Reliability was quantified using concordance correlation coefficient (CCC) and smallest detectable change (SDC). RESULTS Substantial concordance was achieved in prefrontal electrodes at 40 and 60 ms, centroparietal and left parietal ROIs at 100 ms, and central electrodes at 200 ms. Minimum SDC was found in the same regions and peaks, particularly for the peaks at 100 and 200 ms. CCC, but not SDC, reached optimal values by 60-100 pulses per run with saturation beyond this number, while SDC continued to improve with increased pulse numbers. CONCLUSION TEPs were robust and reliable, requiring a relatively small number of trials to achieve stability, and are thus well suited as outcomes in clinical biomarker or interventional studies.

Mixed Effects Models for Resampled Network Statistics Improves Statistical Power to Find Differences in Multi-Subject Functional Connectivity

Many complex brain disorders, such as autism spectrum disorders, exhibit a wide range of symptoms and disability. To understand how brain communication is impaired in such conditions, functional connectivity studies seek to understand individual differences in brain network structure in terms of covariates that measure symptom severity. In practice, however, functional connectivity is not observed but estimated from complex and noisy neural activity measurements. Imperfect subject network estimates can compromise subsequent efforts to detect covariate effects on network structure. We address this problem in the case of Gaussian graphical models of functional connectivity, by proposing novel two-level models that treat both subject level networks and population level covariate effects as unknown parameters. To account for imperfectly estimated subject level networks when fitting these models, we propose two related approaches—R2 based on resampling and random effects test statistics, and R3 that additionally employs random adaptive penalization. Simulation studies using realistic graph structures reveal that R2 and R3 have superior statistical power to detect covariate effects compared to existing approaches, particularly when the number of within subject observations is comparable to the size of subject networks. Using our novel models and methods to study parts of the ABIDE dataset, we find evidence of hypoconnectivity associated with symptom severity in autism spectrum disorders, in frontoparietal and limbic systems as well as in anterior and posterior cingulate cortices.

Randomized Approach to Differential Inference in Multi-subject Functional Connectivity

Inferring functional connectivity, or statistical dependencies between activity in different regions of the brain, is of great interest in the study of neurocognitive conditions. For example, studies [1]-[3] indicate that patterns in connectivity might yield potential biomarkers for conditions such as Alzheimers and autism. We model functional connectivity using Markov Networks, which use conditional dependence to determine when brain regions are directly connected. In this paper, we show that standard large-scale two-sample testing that compares graphs from distinct populations using subject level estimates of functional connectivity, fails to detect differences in functional connections. We propose a novel procedure to conduct two-sample inference via resampling and randomized edge selection to detect differential connections, with substantial improvement in statistical power and error control.

Mixed Effects Models to Find Differences in Multi-Subject Functional Connectivity

Many complex brain disorders such as autism spectrum disorders exhibit a wide range of symptoms and disability. To understand how brain communication is impaired in such conditions, functional connectivity studies seek to understand individual differences in brain network structure in terms of covariates that measure symptom severity. In practice, however, functional connectivity is not observed but estimated from complex and noisy neural activity measurements. Imperfect subject network estimates can compromise subsequent efforts to detect covariate effects on network structure. We address this problem in the case of Gaussian graphical models of functional connectivity, by proposing novel two-level models that treat both subject level networks and population level covariate effects as unknown parameters. To account for imperfectly estimated subject level networks when fitting these models, we propose two related approaches R^2 & R^3 based on resampling, random adaptive penalization and random effects test statistics. Simulation studies using realistic graph structures reveal that R^2 and R^3 have superior statistical power to detect covariate effects compared to existing approaches, particularly when the number of within subject observations is comparable to the size of subject networks. Using our novel models and methods to study parts of the ABIDE dataset, we find evidence of hypoconnectivity associated with symptom severity in Autism spectrum disorders, in frontoparietal and limbic systems as well as in anterior and posterior cingulate cortices.Many complex brain disorders, such as autism spectrum disorders, exhibit a wide range of symptoms and disability. To understand how brain communication is impaired in such conditions, functional connectivity studies seek to understand individual differences in brain network structure in terms of covariates that measure symptom severity. In practice, however, functional connectivity is not observed but estimated from complex and noisy neural activity measurements. Imperfect subject network estimates can compromise subsequent efforts to detect covariate effects on network structure. We address this problem in the case of Gaussian graphical models of functional connectivity, by proposing novel two-level models that treat both subject level networks and population level covariate effects as unknown parameters. To account for imperfectly estimated subject level networks when fitting these models, we propose two related approaches — R2 based on resampling and random effects test statistics, and R3 that additionally employs random adaptive penalization. Simulation studies using realistic graph structures reveal that R2 and R3 have superior statistical power to detect covariate effects compared to existing approaches, particularly when the number of within subject observations is comparable to the size of subject networks. Using our novel models and methods to study parts of the ABIDE dataset, we find evidence of hypoconnectivity associated with symptom severity in autism spectrum disorders, in frontoparietal and limbic systems as well as in anterior and posterior cingulate cortices.

Suboptimality of nonlocal means on images with sharp edges

We conduct an asymptotic risk analysis of the nonlocal means image denoising algorithm for Horizon class images that are piecewise constant with a sharp edge discontinuity. We prove that the mean-square risk of nonlocal means is suboptimal and in fact is within a log factor of the mean square risk of wavelet thresholding.

Population Inference for Node Level Differences in Multi-subject Functional Connectivity

Using Gaussian graphical models as the basis for functional connectivity, we propose new models and test statistics to detect whether subject covariates predict differences in network metrics in a population of subjects. Our approach emphasizes the need to account for errors in estimating subject level networks when conducting inference at the population level. Using simulations, we show that failure to do so reduces statistical power in detecting covariate effects for realistic graph structures. We illustrate the benefits of our procedure for clinical neuroimaging using a resting-state fMRI study of neurofibromatosis-I.